With the expansion of network scale and network complexity

traditional routing algorithms cannot ensure both the calculation complexity and performance under the large fluctuation of spatial-temporal distribution of network traffic. In recent years

with the development of Software-Defined Networking (SDN) and Artificial Intelligence (AI)

AI-based methods of automatic routing strategies are gaining attention. In this paper

we propose an intelligent network routing technology called SmartPath based on Deep Reinforcement Learning (DRL). With dynamic collection of network status

we can use DRL to generate routing policies automatically

thus ensuring that the routing policy can dynamically adapt to the change of network traffic. Experiment result shows that the proposed scheme can adjust the routing strategy dynamically without human experience on traffic analysis and can reduce the average end-to-end transmission delay by at least 10% compared with the state-of-art schemes.